1. Field of the Invention
The present invention relates to a magnetic resonance diagnosis apparatus, a noise spatial distribution generating method, and a signal acquisition method.
2. Description of the Related Art
Several known methods are available to calculate the SNR (Signal-to-Noise Ratio) or CNR (Contrast-to-Noise Ratio) of an image. The following three methods can be given as typical methods in terms of measurement of noise components.
(1) Difference Method
The differences between the images captured under the same conditions are calculated, and reproducible components (containing Gibbs ringing in addition to signals) are removed from the differences, thereby extracting components corresponding to random noise. The SD (Standard Deviation) of the components is measured. This method is suitable when it is aimed at a phantom. A problem is, however, that it is difficult to repeatedly perform imaging under the same conditions when a person is imaged especially in a clinical case.
(2) Non-Difference Method: Measurement on Region of Interest without Difference Calculation (also Called “Same ROI Method” or the Like)
Signal components are required to be flat. However, MR images often contain low-order spatial components (gradual signal changes) due to various factors, and hence this requirement is difficult to meet. If a person is an object, the requirement is difficult more often to meet because of the inevitable existence of the original anatomical structure. In the case of a person, this problem is difficult to solve, and hence this method cannot be said to be a proper method.
(3) Non-Difference Method: Substitution by Background SD (Also Called “Spatial Noise Method” or the Like)
Although a signal component is measured in a region of interest ROI, a noise component is substituted by the standard deviation SD or average value of a background portion without any signal. A background portion of an absolute-value image has a different noise characteristic, a measured value in the background is converted into the noise amplitude of a signal portion. This method has been used most widely. No significant problems have arisen in the images obtained by a single coil and the sum-of-square method (SOS method, more precisely, the Square Root of Sum-of-Square method) which is a typical image forming method using an array coil.
For example, a background portion B is assumed as a noise spatial distribution, and its standard deviation σ(B) is obtained. Obtaining signal portion conversion value σ′=σ′(B) which changes depending on the number of channels of the array coil from the standard deviation σ(B) can obtain noise representing this image. The SNR of a parenchymal organ 1 can be obtained by dividing an average value m(O1) of an observation signal in a local region by the value σ′ described above, i.e., can be given by m(O1)/σ′. Likewise, the CNR between the parenchymal organ 1 and a parenchymal organ 2 is calculated as (m(O1)−m(O2))/σ′. Clinically, the CNR of a parenchymal organ adjacent to a morbid region L is represented by (m(L)−m(O1))/σ′.
Each technique described above is based on the assumption that the noise intensity in an image is relatively invariable, i.e., uniform. If, therefore, the noise intensity in an image is relatively variable depending on positions in the image, the reliability of conventional noise evaluation deteriorates. For example, with recent advances in parallel imaging (PI) in MRI, noise has not become spatially uniform due to sensitivity irregularity correction processing for a surface array coil used for PI and PI unfolding processing.
In consideration of these situations, evaluation methods have recently been proposed. Reference 5 described below discloses a method of evaluating an SNR at each point of a final image by using added pre-scan data. This method is a strict method but uses information other than a generally obtained final image, and hence cannot be directly used at a clinical site.
On the other hand, as a more practical approach, there is proposed a method of obtaining the SNR or CNR of a final image obtained in inspection by estimating a noise component from the image alone. This is important in terms of clinical inspection or clinical research. Another proposed method is a method of regarding a curved surface approximated in a designated ROI as a proper signal component and using a difference from it as source data for distributed calculation. However, this method has a problem that it is difficult to set an ROI due to an anatomical structure or that if an ROI is set in accordance with an anatomical structure, since the ROI becomes small, the accuracy of distributed estimation deteriorates in statistical terms.
In addition to the above methods, there is available a method which basically calculates a standard deviation SD at each point by using many images captured continuously. This method is obviously premised on temporal reproducibility but can be a most acceptable noise component estimation method at each point. The use of this method is reported in the following reference. However, the method requires continuous imaging, and hence is designed for evaluation limited to part of fast imaging.
A method based on oversampling in read operation has recently been proposed by Steckner. This method is not easily influenced by the movement of an object. However, the cutoff characteristic of a low-pass filter (LPF) sometimes comes into play in the center of an image. If importance is placed on the in-plane distribution of SNRs, therefore, a more reliable method is required.
In general, it is difficult to calculate the SNR or noise index of a recent MRI image containing non-uniform noise components. It is conventionally impossible to perform such calculation in imaging operation for a person whose movement influences the operation without fail. Although there are strong demands for a practical noise spatial distribution calculation method, there is no such method available.    Reference 1: National Electrical Manufacturers Association: Determination of signal-to-noise ratio in diagnostic magnetic resonance imagers, NEMA Standard Publications, MS-1, 2001    Reference 2: Kasai and Doi, “MR Imaging Technology”, Ohmsha, 2003    Reference 3: Ogura, et al., “Basic Study on Measurement of SNR of MR Image”, Japanese Journal of Radiological Technology, 59(4), 508-513, 2003    Reference 4: Pruessmann K S, et al., SENSE: Sensitivity Encoding for Fast MRI, Magnetic Resonance in Medicine 42: 952-962 (1999)    Reference 5: Kellman P, et al., Image reconstruction in SNR units: A general method for SNR measurement, Magnetic Resonance in Medicine 54: 1439-1447 (2005)    Reference 6: Reeder S B, et al., Practical Approaches to the evaluation of signal-to-noise ratio performance with parallel imaging: Application with cardiac imaging and a 32-channel cardiac coil, Magnetic Resonance in Medicine 54: 748-754 (2005)    Reference 7: Steckner M C, A new signal acquisition, two-image difference method for determining MR image SNR, Proc. Intl. Soc. Mag. Reson. Med. 14(2006), p. 2398